Communication systems are known in the art. Many such systems support transmission of data from one location to another. This data will often comprise electronic symbols (such as a symbol having a particular amplitude and/or phase with respect to a baseline metric) that represent particular data quantities or values. Such symbols, when properly received, can be decoded to allow reconstruction of the original message.
Radio frequency communication systems are also known, where such data is transmitted using a radio frequency channel. In a land mobile operating environment, where the sending and receiving unit are moving with respect to one another, channel degradation occurs due to such phenomena as multipath interference, or Rayleigh fading. Such degradation can impact symbol recognition and hence the decoding process, leading to an inaccurate interpretation of the data.
Various methods have been proposed to protect data from channel degradation. Such solutions typically represent a compromise of one sort or the other, and hence have one or more problems associated with their adoption, including costly reception platforms to support computationally intensive recovery schemes, and data throughput reduction (as necessitated, for example, by inclusion of significant error coding or signal diversity of one type or another). These solutions may also suffer from varying standards of reliability as channel conditions vary, as may occur in a short-term faded environment, as when a radio unit temporarily travels through a tunnel.
In U.S. Pat. No. 5,289,504 to Wilson et al, commonly assigned to the assignee of the instant application and incorporated herein by reference, there is disclosed a decoding methodology that addresses at least some of the prior art concerns. In particular, Wilson et al. disclose a method whereby a receiver, in addition to receiving a carrier signal and demodulating it to provide a received information signal, the receiver also processes the carrier signal to determine appropriate channel state metrics. The received information signal and the corresponding calculated channel state metric are both utilized in a disclosed decoding algorithm to recover the original information signal. In the preferred embodiment, the decoding algorithm comprises a Viterbi Algorithm configured to implement a decoder for Trellis Coded Modulation. The carrier signal is processed to calculate the channel state metric information, which information may also be subject to a normalizing step in an effort to potentially minimize subsequent computational requirements. The channel state metric information can also be compensated to accommodate fixed delays that occur in the reception process with respect to determination of the received information signal, and/or may also be subjected to morphological erosion to ensure conservative use of the channel state metric information by the decoding algorithm.
Notable is that Wilson et al explain that the decode processing algorithm may include fixing the channel state metric information output to the decoder at a fixed metric in response to detecting a predetermined channel condition, such as a lack of a faded carrier signal over at least a predetermined period of time. By fixing the channel state metric information during decoding of a signal on a presently static (non-fading) communication channel, Wilson et al is thus able to eliminate error that may occasionally be introduced into a channel state metric calculation by the presence of ordinary channel noise.
As explained in detail in the Wilson patent, decoding of trellis codes or the like symbols transmitted over a carrier signal involves the receiving unit processing the carrier signal to identify the particular amplitude (channel state metric information representative of signal strength) and phase (recovered symbol) information with which reconstruction of the original message could then occur.
All the prior art decoding techniques, including Wilson et al, continue to be highly computationally complex and thus result on substantial battery power consumption. Accordingly, there is a need for an improved decoding solution that results in reduced power consumption and/or computational complexity.